{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.导入特征工程后的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>C1</th>\n",
       "      <th>banner_pos</th>\n",
       "      <th>site_id</th>\n",
       "      <th>site_domain</th>\n",
       "      <th>site_category</th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_domain</th>\n",
       "      <th>app_category</th>\n",
       "      <th>device_model</th>\n",
       "      <th>...</th>\n",
       "      <th>C15</th>\n",
       "      <th>C16</th>\n",
       "      <th>C17</th>\n",
       "      <th>C18</th>\n",
       "      <th>C19</th>\n",
       "      <th>C20</th>\n",
       "      <th>C21</th>\n",
       "      <th>hour_hours</th>\n",
       "      <th>hour_weekday</th>\n",
       "      <th>hour_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   click  C1  banner_pos  site_id  site_domain  site_category  app_id  \\\n",
       "0      0   0           0        1            1              2       0   \n",
       "1      0   0           0        1            1              2       0   \n",
       "2      0   0           0        1            1              2       0   \n",
       "3      0   0           0        1            1              2       0   \n",
       "4      0   0           1       -1           -1             -1       0   \n",
       "\n",
       "   app_domain  app_category  device_model    ...      C15  C16  C17  C18  C19  \\\n",
       "0           0             0            -1    ...        0    0    0    0    0   \n",
       "1           0             0            11    ...        0    0    0    0    0   \n",
       "2           0             0             0    ...        0    0    0    0    0   \n",
       "3           0             0            43    ...        0    0    0    0    0   \n",
       "4           0             0            22    ...        0    0  113    0    0   \n",
       "\n",
       "   C20  C21  hour_hours  hour_weekday  hour_days  \n",
       "0    0    2           0             2          0  \n",
       "1    1    2           0             2          0  \n",
       "2    1    2           0             2          0  \n",
       "3    1    2           0             2          0  \n",
       "4    0    6           0             2          0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train_4.csv\",nrows= 3000,index_col=0)   #拿50万条数据训练\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.GBDT模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['click']\n",
    "x = train.drop([\"click\"], axis=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test logloss of GBDT  : 0.4336803011596203\n"
     ]
    }
   ],
   "source": [
    "# 定义模型\n",
    "xgboost = xgb.XGBClassifier(nthread=4, learning_rate=0.08,\n",
    "                        n_estimators=20, max_depth=2, gamma=0, subsample=0.5, colsample_bytree=0.7)\n",
    "# 训练学习\n",
    "xgboost.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "# 预测及logloss评测\n",
    "y_pred_gbdt = xgboost.predict_proba(x_test)\n",
    "logloss_gbdt = log_loss(y_test, y_pred_gbdt)\n",
    "print (\"test logloss of GBDT  :\",logloss_gbdt )\n",
    "\n",
    "fpr_gbdt, tpr_gbdt, _ = roc_curve(y_test, y_pred_gbdt[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_gbdt, tpr_gbdt, _ = roc_curve(y_test, y_pred_gbdt[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用训练好GBDT，对原始特征编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集编码后size: (2100, 20)\n",
      "验证集编码后size: (900, 20)\n"
     ]
    }
   ],
   "source": [
    "# xgboost编码原有特征。变为【数据条数:树的数目】\n",
    "#如果用gbdt(GradientBoostingClassifier)，apply()的结果是三维的,[数据条数:树的数目:叶子节点数]x_train_leaves = xgboost.apply(x_train)[:,:,0]\n",
    "x_train_leaves = xgboost.apply(x_train)     \n",
    "x_test_leaves = xgboost.apply(x_test) \n",
    "\n",
    "print(\"训练集编码后size:\",x_train_leaves.shape)\n",
    "print(\"验证集编码后size:\",x_test_leaves.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于自己实现的FFM模型,参数指定是dataFrame，这里需要先转换格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ndarray 转dataFrame\n",
    "x_train_leaves = pd.DataFrame(x_train_leaves)            \n",
    "x_test_leaves = pd.DataFrame(x_test_leaves)\n",
    "y_train=pd.DataFrame(y_train)\n",
    "y_test=pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "调用预处理方法,one_hot，同时得到ffm_dic字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集onehot后size: (2100, 80)\n",
      "验证集onehot后size: (900, 80)\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "import ffm_data_process                #FFM前的数据预处理(内含one-hot)\n",
    "\n",
    "x_train,x_test,feature_n,filed_m,ffm_dic = ffm_data_process.onehot_get_dict(x_train_leaves,x_test_leaves)\n",
    "# del x_train_leaves,x_test_leaves\n",
    "print(\"训练集onehot后size:\",x_train.shape)\n",
    "print(\"验证集onehot后size:\",x_test.shape)\n",
    "print(type(x_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.GBDT+LR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:1228: UserWarning: 'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = -1.\n",
      "  \" = {}.\".format(self.n_jobs))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=-1,\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# 定义LR模型\n",
    "lr = LogisticRegression(n_jobs = -1, C=0.1, penalty='l1',solver='liblinear')\n",
    "# lr对gbdt特征编码后的样本模型训练\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogLoss: 0.4083651856921999\n"
     ]
    }
   ],
   "source": [
    "# 预测及LogLoss评测\n",
    "y_pred_lr = lr.predict_proba(x_test)\n",
    "logloss = log_loss(y_test, y_pred_lr)\n",
    "print('LogLoss:' , logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_lr, tpr_lr, _ = roc_curve(y_test, y_pred_lr[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.GBDT+FTRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 i= 0\tloss=0.6931471805599453\n",
      "epoch: 0 i= 500\tloss=0.4045771321943643\n",
      "epoch: 0 i= 1000\tloss=0.07185249017502504\n",
      "epoch: 0 i= 1500\tloss=0.06828153434983945\n",
      "epoch: 0 i= 2000\tloss=0.07474042737092866\n",
      "epoch: 1 i= 0\tloss=0.0812166737835196\n",
      "epoch: 1 i= 500\tloss=0.3529765289959689\n",
      "epoch: 1 i= 1000\tloss=0.058696473553055746\n",
      "epoch: 1 i= 1500\tloss=0.07565438173572854\n",
      "epoch: 1 i= 2000\tloss=0.07534510460059\n",
      "epoch: 2 i= 0\tloss=0.08410749364629129\n",
      "epoch: 2 i= 500\tloss=0.33209414774625656\n",
      "epoch: 2 i= 1000\tloss=0.0566121848102279\n",
      "epoch: 2 i= 1500\tloss=0.07834759712650236\n",
      "epoch: 2 i= 2000\tloss=0.07529840882254107\n",
      "epoch: 3 i= 0\tloss=0.08633552483655055\n",
      "epoch: 3 i= 500\tloss=0.319674267457581\n",
      "epoch: 3 i= 1000\tloss=0.05625948351590896\n",
      "epoch: 3 i= 1500\tloss=0.08005885888079328\n",
      "epoch: 3 i= 2000\tloss=0.07521896849388567\n",
      "epoch: 4 i= 0\tloss=0.08826297534714939\n",
      "epoch: 4 i= 500\tloss=0.3111714455854042\n",
      "epoch: 4 i= 1000\tloss=0.05639203956116984\n",
      "epoch: 4 i= 1500\tloss=0.08136558727942926\n",
      "epoch: 4 i= 2000\tloss=0.07516273873965337\n",
      "epoch: 5 i= 0\tloss=0.08999713765617141\n",
      "epoch: 5 i= 500\tloss=0.30486876488273074\n",
      "epoch: 5 i= 1000\tloss=0.05667564597986949\n",
      "epoch: 5 i= 1500\tloss=0.08243879371335187\n",
      "epoch: 5 i= 2000\tloss=0.07512902591909751\n",
      "epoch: 6 i= 0\tloss=0.09158937687649667\n",
      "epoch: 6 i= 500\tloss=0.29996041671001056\n",
      "epoch: 6 i= 1000\tloss=0.05699850748473538\n",
      "epoch: 6 i= 1500\tloss=0.08335379890427379\n",
      "epoch: 6 i= 2000\tloss=0.07511054856758065\n",
      "epoch: 7 i= 0\tloss=0.0930694402114361\n",
      "epoch: 7 i= 500\tloss=0.296003702467066\n",
      "epoch: 7 i= 1000\tloss=0.0573187061821138\n",
      "epoch: 7 i= 1500\tloss=0.08415173607246637\n",
      "epoch: 7 i= 2000\tloss=0.07510233410918753\n",
      "epoch: 8 i= 0\tloss=0.09445778314464119\n",
      "epoch: 8 i= 500\tloss=0.29273178826523505\n",
      "epoch: 8 i= 1000\tloss=0.057620642611327044\n",
      "epoch: 8 i= 1500\tloss=0.08485115663529795\n",
      "epoch: 8 i= 2000\tloss=0.07510114552551984\n",
      "epoch: 9 i= 0\tloss=0.09576992437388125\n",
      "epoch: 9 i= 500\tloss=0.2899729330258232\n",
      "epoch: 9 i= 1000\tloss=0.05789940597716933\n",
      "epoch: 9 i= 1500\tloss=0.08548398615952235\n",
      "epoch: 9 i= 2000\tloss=0.07510468449047586\n",
      "time: 105.18300008773804\n"
     ]
    }
   ],
   "source": [
    "import FTRL\n",
    "d = x_train.shape[1] \n",
    "ftrl = FTRL.FTRL(dim=d, l1=0.01, l2=0.01, alpha=0.08, beta=1.0)\n",
    "start = time.time()\n",
    "ftrl.train(x_train, y_train, min_loss=0.01, epochs=10,early_stop = 100,print_loss = True)\n",
    "end = time.time()\n",
    "print(\"time:\",str(end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test logloss of FTRL  : 0.40451622581062024\n"
     ]
    }
   ],
   "source": [
    "y_pred_ftrl = ftrl.predict(x_test)\n",
    "logloss_ftrl = log_loss(y_test, y_pred_ftrl)\n",
    "print (\"test logloss of FTRL  :\",logloss_ftrl )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_ftrl, tpr_ftrl, _ = roc_curve(y_test, y_pred_ftrl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.GBDT+FFM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FFM的时间复杂度O(KD²)，训练很慢"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0,第: 0 条数据训练\n",
      "epoch: 0,第: 500 条数据训练\n",
      "epoch: 0,第: 1000 条数据训练\n",
      "epoch: 0,第: 1500 条数据训练\n",
      "epoch: 0,第: 2000 条数据训练\n",
      "epoch: 0, train_loss: 0.4526360085335718\n",
      "epoch: 1,第: 0 条数据训练\n",
      "epoch: 1,第: 500 条数据训练\n",
      "epoch: 1,第: 1000 条数据训练\n",
      "epoch: 1,第: 1500 条数据训练\n",
      "epoch: 1,第: 2000 条数据训练\n",
      "epoch: 1, train_loss: 0.4508536190075116\n",
      "epoch: 2,第: 0 条数据训练\n",
      "epoch: 2,第: 500 条数据训练\n",
      "epoch: 2,第: 1000 条数据训练\n",
      "epoch: 2,第: 1500 条数据训练\n",
      "epoch: 2,第: 2000 条数据训练\n",
      "epoch: 2, train_loss: 0.44967982807405915\n",
      "epoch: 3,第: 0 条数据训练\n",
      "epoch: 3,第: 500 条数据训练\n",
      "epoch: 3,第: 1000 条数据训练\n",
      "epoch: 3,第: 1500 条数据训练\n",
      "epoch: 3,第: 2000 条数据训练\n",
      "epoch: 3, train_loss: 0.44901627881014045\n",
      "epoch: 4,第: 0 条数据训练\n",
      "epoch: 4,第: 500 条数据训练\n",
      "epoch: 4,第: 1000 条数据训练\n",
      "epoch: 4,第: 1500 条数据训练\n",
      "epoch: 4,第: 2000 条数据训练\n",
      "epoch: 4, train_loss: 0.4488039249398546\n",
      "epoch: 5,第: 0 条数据训练\n",
      "epoch: 5,第: 500 条数据训练\n",
      "epoch: 5,第: 1000 条数据训练\n",
      "epoch: 5,第: 1500 条数据训练\n",
      "epoch: 5,第: 2000 条数据训练\n",
      "epoch: 5, train_loss: 0.44901665025757515\n",
      "time: 2119.8010001182556\n"
     ]
    }
   ],
   "source": [
    "from FFM import FFMClassifier  \n",
    "import time\n",
    "k = 4\n",
    "eta = 0.2\n",
    "lambd = 2e-5\n",
    "max_echo = 6   \n",
    "min_loss =0.2\n",
    "ffm = FFMClassifier(feature_n, filed_m , k , eta , lambd , ffm_dic )\n",
    "start = time.time()\n",
    "ffm.train(x_train , y_train, max_echo , min_loss)\n",
    "end = time.time()\n",
    "print(\"time:\",str(end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test logloss of FFM  : 0.44444780372262055\n"
     ]
    }
   ],
   "source": [
    "y_pred_ffm = ffm.predict(x_test)\n",
    "logloss_ffm = log_loss(y_test,y_pred_ffm)\n",
    "print (\"test logloss of FFM  :\",logloss_ffm )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_ffm, tpr_ffm, _ = roc_curve(y_test, y_pred_ffm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.图形展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2ae400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b7ede48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_gbdt, tpr_gbdt, label='GBDT')\n",
    "plt.plot(fpr_ftrl, tpr_ftrl, label='GBDT + FTRL')\n",
    "plt.plot(fpr_ffm, tpr_ffm, label='GBDT+FFM')\n",
    "plt.plot(fpr_lr, tpr_lr, label='GBDT+LR')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(2)\n",
    "plt.xlim(0.5, 1.0)\n",
    "plt.ylim(0.8, 1.0)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_gbdt, tpr_gbdt, label='GBDT')\n",
    "plt.plot(fpr_ftrl, tpr_ftrl, label='GBDT + FTRL')\n",
    "plt.plot(fpr_ffm, tpr_ffm, label='GBDT+FFM')\n",
    "plt.plot(fpr_lr, tpr_lr, label='GBDT+LR')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve (zoomed in at top left)')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
